Pet owners versus no-pet owners

PetOwner (1:Yes; 0:No) : Do you currently have or share responsibility for a pet/companion animal?

Common Questions

Data Size:

table(alldata$Wave)

  1   2   3   4 
718 565 472 428

genhealth

Ordinal Logistic Regression: genhealth ~ PetOwner + Wave + if_shelter + PetOwner * if_shelter + (1 | workerId)
Model Estimate Std. Error pvalue
PetOwnerYes -0.637 0.532 0.232
Wave2 0.033 0.177 0.853
Wave3 0.379 0.194 0.051
Wave4 0.282 0.252 0.263
if_shelteryes -0.237 0.369 0.52
PetOwnerYes:if_shelteryes 0.381 0.414 0.357

Kessler 1-10

only show one plot here for example

Ordinal Logistic Regression: PetOwner + Wave + if_shelter + PetOwner*if_shelter + (1|workerId)
Model Estimate Std. Error pvalue
Kessler1: how often did you feel tired for no good reason?
PetOwnerYes 0.059 0.369 0.874
Wave2 -0.724 0.129 0
Wave3 -0.821 0.142 0
Wave4 -0.705 0.183 0
if_shelteryes -0.215 0.264 0.416
PetOwnerYes:if_shelteryes 0.151 0.297 0.611
Kessler2: how often did you feel nervous?
PetOwnerYes -0.001 0.340 0.998
Wave2 -0.761 0.132 0
Wave3 -1.540 0.147 0
Wave4 -1.425 0.183 0
if_shelteryes -0.410 0.244 0.093
PetOwnerYes:if_shelteryes 0.208 0.296 0.482
Kessler3: how often did you feel so nervous that nothing could calm you down?
PetOwnerYes -0.647 0.475 0.173
Wave2 -0.252 0.159 0.113
Wave3 -0.892 0.183 0
Wave4 -1.089 0.238 0
if_shelteryes -0.613 0.350 0.08
PetOwnerYes:if_shelteryes 0.534 0.399 0.18
Kessler4: how often did you feel hopeless?
PetOwnerYes -0.469 0.414 0.257
Wave2 -0.560 0.143 0
Wave3 -0.953 0.161 0
Wave4 -1.001 0.208 0
if_shelteryes -0.547 0.299 0.067
PetOwnerYes:if_shelteryes 0.125 0.342 0.714
Kessler5: how often did you feel restless or fidgety?
PetOwnerYes 0.076 0.347 0.827
Wave2 -0.519 0.127 0
Wave3 -0.998 0.142 0
Wave4 -1.354 0.186 0
if_shelteryes -0.352 0.262 0.179
PetOwnerYes:if_shelteryes 0.251 0.296 0.396
Kessler6: how often did you feel so restless that you could not sit still?
PetOwnerYes -0.516 0.430 0.23
Wave2 -0.611 0.154 0
Wave3 -0.847 0.172 0
Wave4 -1.213 0.227 0
if_shelteryes -0.678 0.326 0.037
PetOwnerYes:if_shelteryes 0.469 0.366 0.2
Kessler7: how often did you feel depressed?
PetOwnerYes 0.093 0.452 0.838
Wave2 -0.646 0.143 0
Wave3 -0.876 0.160 0
Wave4 -1.200 0.212 0
if_shelteryes -0.375 0.299 0.209
PetOwnerYes:if_shelteryes -0.055 0.335 0.869
Kessler8: how often did you feel so depressed that nothing could cheer you up?
PetOwnerYes -0.518 0.481 0.282
Wave2 -0.520 0.162 0.001
Wave3 -0.940 0.187 0
Wave4 -1.195 0.244 0
if_shelteryes -0.786 0.345 0.023
PetOwnerYes:if_shelteryes 0.210 0.393 0.593
Kessler9: how often did you feel that everything was an effort?
PetOwnerYes 0.232 0.434 0.593
Wave2 -0.315 0.138 0.022
Wave3 -0.647 0.154 0
Wave4 -0.565 0.200 0.005
if_shelteryes -0.183 0.292 0.531
PetOwnerYes:if_shelteryes -0.326 0.328 0.321
Kessler10: how often did you feel worthless?
PetOwnerYes -0.920 0.483 0.057
Wave2 -0.321 0.164 0.05
Wave3 -0.714 0.185 0
Wave4 -0.883 0.235 0
if_shelteryes -1.224 0.337 0
PetOwnerYes:if_shelteryes 0.808 0.390 0.038

Now let’s look at the sum of Kessler 1-10

Categorize the sum of Kessler 1-10

Ordinal Logistic Regression: Kessler ~ PetOwner + Wave + if_shelter + PetOwner * if_shelter + (1 | workerId)
Model Estimate Std. Error pvalue
PetOwnerYes -0.873 0.610 0.152
Wave2 -0.924 0.176 0
Wave3 -1.359 0.198 0
Wave4 -1.657 0.262 0
if_shelteryes -1.417 0.371 0
PetOwnerYes:if_shelteryes 0.755 0.423 0.074

If we didn’t group this variable and treat it as a continuous variable, here is the result of linear mixed effect model.

Linear mixed-effect model: Kessler_Sum ~ PetOwner + Wave + if_shelter + PetOwner * if_shelter + (1 | workerId)
Model Estimate Std. Error pvalue
PetOwnerYes -0.404 0.686 0.556
Wave2 -0.995 0.167 0
Wave3 -1.717 0.183 0
Wave4 -1.865 0.239 0
if_shelteryes -0.866 0.355 0.015
PetOwnerYes:if_shelteryes 0.304 0.397 0.444

Gad 1-7

only show one plot here for example

Ordinal Logistic Regression: Grad ~ PetOwner + Wave + if_shelter + PetOwner*if_shelter + (1|workerId)
Model Estimate Std. Error pvalue
Gad1: how often have you been bothered by feeling nervous, anxious or on edge?
PetOwnerYes 0.189 0.397 0.633
Wave2 -0.502 0.132 0
Wave3 -1.066 0.149 0
Wave4 -1.048 0.192 0
if_shelteryes -0.228 0.278 0.411
PetOwnerYes:if_shelteryes 0.220 0.312 0.48
Gad2: how often have you been bothered by not being able to stop or control worrying?
PetOwnerYes -0.204 0.449 0.649
Wave2 -0.600 0.145 0
Wave3 -0.770 0.162 0
Wave4 -0.896 0.207 0
if_shelteryes -0.353 0.316 0.263
PetOwnerYes:if_shelteryes 0.217 0.357 0.544
Gad3: how often have you been bothered by worrying too much about different things?
PetOwnerYes 0.543 0.396 0.171
Wave2 -0.520 0.132 0
Wave3 -0.940 0.147 0
Wave4 -0.904 0.190 0
if_shelteryes 0.151 0.285 0.597
PetOwnerYes:if_shelteryes -0.378 0.318 0.234
Gad4: how often have you had trouble relaxing?
PetOwnerYes -0.009 0.388 0.982
Wave2 -0.550 0.133 0
Wave3 -0.780 0.145 0
Wave4 -1.025 0.191 0
if_shelteryes -0.226 0.275 0.411
PetOwnerYes:if_shelteryes -0.180 0.308 0.558
Gad5: how often have you been so restless that it’s hard to sit still?
PetOwnerYes -0.170 0.449 0.705
Wave2 -0.268 0.151 0.077
Wave3 -0.727 0.171 0
Wave4 -0.930 0.226 0
if_shelteryes -0.582 0.330 0.077
PetOwnerYes:if_shelteryes 0.436 0.366 0.234
Gad6: how often have you become easily annoyed or irritable?
PetOwnerYes 0.135 0.393 0.73
Wave2 -0.078 0.131 0.551
Wave3 -0.506 0.144 0
Wave4 -0.631 0.188 0.001
if_shelteryes -0.374 0.275 0.174
PetOwnerYes:if_shelteryes 0.364 0.309 0.239
Gad7: how often have you been bothered by feeling afraid as if something awful might happen?
PetOwnerYes -0.076 0.411 0.853
Wave2 -0.573 0.138 0
Wave3 -1.167 0.156 0
Wave4 -1.102 0.202 0
if_shelteryes 0.128 0.310 0.68
PetOwnerYes:if_shelteryes 0.019 0.346 0.957

Now let’s look at the sum of Gad 1-7

Linear mixed-effect model: GAD_Sum ~ PetOwner + Wave + if_shelter + PetOwner * if_shelter + (1 | workerId)
Model Estimate Std. Error pvalue
PetOwnerYes 0.037 0.534 0.945
Wave2 -0.643 0.140 0
Wave3 -1.260 0.154 0
Wave4 -1.325 0.201 0
if_shelteryes -0.286 0.297 0.336
PetOwnerYes:if_shelteryes 0.119 0.332 0.721

Lonely 1-3

Ordinal Logistic Regression: Lonely ~ PetOwner + Wave + if_shelter + PetOwner*if_shelter + (1|workerId)
Model Estimate Std. Error pvalue
Lonely1: how often did you feel you lack companionship?
PetOwnerYes -1.504 0.454 0.001
Wave2 -0.342 0.141 0.015
Wave3 -0.124 0.155 0.423
Wave4 -0.374 0.203 0.065
if_shelteryes -0.302 0.288 0.294
PetOwnerYes:if_shelteryes 0.708 0.337 0.036
Lonely2: how often did you feel left out?
PetOwnerYes -0.995 0.304 0.001
Wave2 -0.152 0.001 0
Wave3 -0.161 0.001 0
Wave4 -0.425 0.001 0
if_shelteryes -0.213 0.001 0
PetOwnerYes:if_shelteryes 0.153 0.224 0.495
Lonely3: how often did you feel isolated from others?
PetOwnerYes -0.601 0.357 0.092
Wave2 -0.572 0.124 0
Wave3 -0.749 0.136 0
Wave4 -1.114 0.178 0
if_shelteryes -0.058 0.253 0.818
PetOwnerYes:if_shelteryes 0.480 0.291 0.1

Now let’s look at the sum of Lonely 1-3

Linear mixed-effect model: Loneliness ~ PetOwner + Wave + if_shelter + PetOwner * if_shelter + (1 | workerId)
Model Estimate Std. Error pvalue
PetOwnerYes -0.663 0.269 0.014
Wave2 -0.269 0.077 0
Wave3 -0.269 0.084 0.001
Wave4 -0.488 0.109 0
if_shelteryes -0.152 0.161 0.345
PetOwnerYes:if_shelteryes 0.287 0.180 0.112

Risk 1-3

Ordinal Logistic Regression: Risk ~ PetOwner + Wave + if_shelter + PetOwner*if_shelter + (1|workerId)
Model Estimate Std. Error pvalue
Risk1:In your opinion, how likely is it that you will contract COVID-19?
PetOwnerYes 0.171 0.371 0.645
Wave2 -0.049 0.117 0.679
Wave3 -0.463 0.129 0
Wave4 -0.539 0.169 0.001
if_shelteryes -0.287 0.248 0.247
PetOwnerYes:if_shelteryes 0.260 0.277 0.349
Risk2:How serious do you think COVID-19 would be if you contracted it?
PetOwnerYes 0.834 0.389 0.032
Wave2 -0.496 0.128 0
Wave3 -0.596 0.139 0
Wave4 -1.046 0.182 0
if_shelteryes 0.245 0.265 0.355
PetOwnerYes:if_shelteryes -0.644 0.298 0.03
Risk3:How concerned are you about the COVID-19?
PetOwnerYes -0.089 0.430 0.836
Wave2 -0.899 0.136 0
Wave3 -1.481 0.152 0
Wave4 -1.879 0.197 0
if_shelteryes -0.200 0.275 0.468
PetOwnerYes:if_shelteryes 0.115 0.309 0.711

Now let’s look at the sum of Risk 1-3

Model Estimate Std. Error pvalue
PetOwnerYes 0.243 0.257 0.344
Wave2 -0.333 0.072 0
Wave3 -0.653 0.079 0
Wave4 -0.870 0.103 0
if_shelteryes -0.093 0.152 0.538
PetOwnerYes:if_shelteryes -0.029 0.170 0.864

Unique question for wave 3 and wave 4

Aware

Ordinal Logistic Regression: Aware ~ PetOwner + Wave + (1|workerId)
Model Estimate Std. Error pvalue
PetOwnerYes -0.049 0.223 0.827
Wave4 -0.115 0.147 0.432

TrustInfo

Summary table for ‘TrustInfo’ and ‘InfoSource’

Not at all or Slightly trustworthy Moderately trustworthy Very or Extremely trustworthy
Media/new feeds 123 (22.2%) 260 (46.93%) 171 (30.87%)
Social media / Instagram / Facebook groups / twitter 83 (46.37%) 72 (40.22%) 24 (13.41%)
Government and regulatory agencies 4 (7.55%) 20 (37.74%) 29 (54.72%)
Friends / family 16 (40%) 17 (42.5%) 7 (17.5%)

SusceptAnimal 1-7 and Q92 1-9

  • SusceptAnimal_1-7: The question realted to these 7 variables is: Based on your current knowledge, rate the extent to which you think each of the following animals can “catch” COVID-19. There are 7 animals listed in this question.
  • Q92_1-9: The question realted to these 9 variables is the same as the above. There are 9 animals listed in this question.

Therefore, they are the same questions but different animals listed.

Pre:

  1. Re-categorize the outcome (SusceptAnimal/Q92)
  • cannot catch: Definitely cannot catch COVID-19; Probably cannot catch COVID-19; Might or might not catch COVID-19
  • can catch: Probably can catch COVID-19; Definitely can catch COVID-19
  1. Only use the major info source and use ‘no information source’ as the reference level in the model.
table(sub_dat$InfoSource)

                               No Information Source 
                                                  52 
                                     Media/new feeds 
                                                 554 
Social media / Instagram / Facebook groups / twitter 
                                                 179 
                  Government and regulatory agencies 
                                                  53 
                                    Friends / family 
                                                  40

We build two models with different independent variables. One is PetOwner, another is InfoSource.

  • SusceptAnimal ~ PetOwner + (1|workerId)

show 3 plots here for example

Coef of ‘PetOwnerYes’ in the Logistic Regression: SusceptAnimal ~ PetOwner + (1|workerId) for different animals
Model Estimate Std. Error pvalue
Tigers
PetOwnerYes 1.248 0.418 0.003
Chimpanzees
PetOwnerYes -0.220 0.328 0.502
Gorillas
PetOwnerYes -0.214 0.286 0.453
Deer
PetOwnerYes -1.364 0.411 0.001
Lions
PetOwnerYes 0.817 0.455 0.073
Birds
PetOwnerYes -0.568 0.328 0.084
Bats
PetOwnerYes 0.380 0.340 0.263
Dogs
PetOwnerYes -0.185 0.349 0.596
Cats
PetOwnerYes 0.499 0.369 0.177
Ferrets
PetOwnerYes -0.506 0.351 0.149
Rabbits
PetOwnerYes -0.678 0.387 0.079
Hamsters
PetOwnerYes -0.766 0.341 0.025
Horses
PetOwnerYes -0.851 0.386 0.027
Pigs
PetOwnerYes -0.735 0.320 0.022
Cattle
PetOwnerYes -0.700 0.389 0.072
Chickens
PetOwnerYes -1.323 0.496 0.008
  • SusceptAnimal ~ InfoSource + (1|workerId)

show 2 plots here for example

Coef of ‘InfoSource’ in the Logistic Regression: SusceptAnimal ~ InfoSource + (1|workerId) for different animals
Model Estimate Std. Error pvalue
Tigers
InfoSourceMedia/new feeds 1.695 0.668 0.011
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.323 0.740 0.663
InfoSourceGovernment and regulatory agencies 0.531 0.907 0.558
InfoSourceFriends / family 0.832 0.946 0.379
Chimpanzees
InfoSourceMedia/new feeds 0.827 0.582 0.155
InfoSourceSocial media / Instagram / Facebook groups / twitter 1.388 0.644 0.031
InfoSourceGovernment and regulatory agencies 0.433 0.781 0.579
InfoSourceFriends / family 1.233 0.824 0.135
Gorillas
InfoSourceMedia/new feeds 0.882 0.543 0.104
InfoSourceSocial media / Instagram / Facebook groups / twitter 1.139 0.592 0.054
InfoSourceGovernment and regulatory agencies 0.325 0.724 0.654
InfoSourceFriends / family 1.489 0.765 0.052
Deer
InfoSourceMedia/new feeds -0.645 0.676 0.339
InfoSourceSocial media / Instagram / Facebook groups / twitter -0.176 0.742 0.813
InfoSourceGovernment and regulatory agencies -0.317 0.919 0.73
InfoSourceFriends / family 0.870 0.942 0.356
Lions
InfoSourceMedia/new feeds 1.568 0.730 0.032
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.393 0.818 0.631
InfoSourceGovernment and regulatory agencies 0.155 1.004 0.877
InfoSourceFriends / family 1.147 1.042 0.271
Birds
InfoSourceMedia/new feeds -0.350 0.581 0.547
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.433 0.627 0.489
InfoSourceGovernment and regulatory agencies -0.161 0.793 0.839
InfoSourceFriends / family 0.130 0.826 0.875
Bats
InfoSourceMedia/new feeds 1.443 0.570 0.011
InfoSourceSocial media / Instagram / Facebook groups / twitter 1.553 0.633 0.014
InfoSourceGovernment and regulatory agencies 1.468 0.782 0.06
InfoSourceFriends / family 0.351 0.813 0.666
Dogs
InfoSourceMedia/new feeds 0.674 0.588 0.252
InfoSourceSocial media / Instagram / Facebook groups / twitter 1.009 0.652 0.122
InfoSourceGovernment and regulatory agencies 0.740 0.802 0.356
InfoSourceFriends / family 0.046 0.869 0.958
Cats
InfoSourceMedia/new feeds 0.983 0.593 0.097
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.724 0.655 0.268
InfoSourceGovernment and regulatory agencies -0.223 0.832 0.789
InfoSourceFriends / family -0.276 0.876 0.752
Ferrets
InfoSourceMedia/new feeds 0.299 0.645 0.643
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.585 0.705 0.406
InfoSourceGovernment and regulatory agencies 0.114 0.872 0.896
InfoSourceFriends / family 0.342 0.917 0.709
Rabbits
InfoSourceMedia/new feeds -0.500 0.663 0.45
InfoSourceSocial media / Instagram / Facebook groups / twitter -0.021 0.728 0.977
InfoSourceGovernment and regulatory agencies -0.642 0.915 0.483
InfoSourceFriends / family 0.519 0.937 0.58
Hamsters
InfoSourceMedia/new feeds -0.065 0.612 0.916
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.301 0.668 0.652
InfoSourceGovernment and regulatory agencies -0.690 0.866 0.426
InfoSourceFriends / family -0.057 0.883 0.948
Horses
InfoSourceMedia/new feeds -0.039 0.670 0.953
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.023 0.735 0.975
InfoSourceGovernment and regulatory agencies -0.218 0.910 0.811
InfoSourceFriends / family 0.202 0.944 0.83
Pigs
InfoSourceMedia/new feeds 0.253 0.583 0.664
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.830 0.633 0.19
InfoSourceGovernment and regulatory agencies 0.051 0.792 0.949
InfoSourceFriends / family 0.409 0.818 0.617
Cattle
InfoSourceMedia/new feeds -0.244 0.667 0.715
InfoSourceSocial media / Instagram / Facebook groups / twitter -0.100 0.735 0.892
InfoSourceGovernment and regulatory agencies -0.355 0.923 0.701
InfoSourceFriends / family 0.842 0.939 0.37
Chickens
InfoSourceMedia/new feeds -0.610 0.764 0.425
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.388 0.841 0.645
InfoSourceGovernment and regulatory agencies 0.086 1.060 0.935
InfoSourceFriends / family 0.092 1.080 0.932

We created a new variable called SusceptAnimalCorrect (0: wrong; 1: right), which means whether those people answered these questions right or wrong. Then. we pull the data of different animals together to build the following model:

  • SusceptAnimalCorrect ~ PetOwner + InfoSource + Animal + Wave + (1|workerId)

  • SusceptAnimalCorrect (reference level is ‘wrong’)
  • Animals (refernce level is ‘Dog’): Since about half of the people think dogs can catch COVID-19 and half think cannot, I use ‘Dogs’ as the reference level.
Logistic Regression: SusceptAnimalCorrect ~ PetOwner + InfoSource + Animals + Wave + (1 | workerId)
Model Estimate Std. Error pvalue
PetOwnerYes 0.279 0.064 0
InfoSourceMedia/new feeds 0.234 0.108 0.03
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.033 0.119 0.784
InfoSourceGovernment and regulatory agencies 0.157 0.149 0.292
InfoSourceFriends / family -0.138 0.157 0.378
AnimalsTigers 0.116 0.099 0.241
AnimalsChimpanzees 0.023 0.099 0.82
AnimalsGorillas 0.175 0.099 0.078
AnimalsDeer 1.560 0.114 0
AnimalsLions -0.171 0.099 0.085
AnimalsBirds 1.546 0.114 0
AnimalsBats 0.700 0.102 0
AnimalsCats 0.301 0.100 0.003
AnimalsFerrets -1.463 0.111 0
AnimalsRabbits 1.277 0.109 0
AnimalsHamsters 1.458 0.112 0
AnimalsHorses 1.326 0.110 0
AnimalsPigs 1.038 0.105 0
AnimalsCattle 1.231 0.108 0
AnimalsChickens 1.531 0.113 0
Wave4 -0.021 0.041 0.599

InfectHumans 1-5

Based on your current knowledge, rate the extent to which the following animals have the ability to give COVID-19 to humans.

Re-categorize the outcome (InfectHumans 1-5) - cannot give COVID-19 to humans: Definitely/Probably/might cannot give COVID-19 to humans - can give COVID-19 to humans: Probably/Definitely can give COVID-19 to humans

We build two models with different independent variables. The outcome is InfectHumans (reference level is ‘cannot give CIP-19 to humans’).

  • InfectHumans ~ PetOwner + (1|workerId)
  • InfectHumans ~ InfoSource + (1|workerId)
Coef of ‘PetOwnerYes’ in the Logistic Regression: InfectHumans ~ PetOwner + (1|workerId) for different animals
Model Estimate Std. Error pvalue
Cats
PetOwnerYes 0.230 0.397 0.562
Dogs
PetOwnerYes -0.039 0.367 0.916
Pigs
PetOwnerYes -1.057 0.472 0.025
Chickens
PetOwnerYes -0.846 0.352 0.016
Tigers
PetOwnerYes 0.171 0.430 0.692
Coef of ‘InfoSource’ in the Logistic Regression: InfectHumans ~ InfoSource + (1|workerId) for different animals
Model Estimate Std. Error pvalue
Cats
InfoSourceMedia/new feeds -0.085 0.678 0.9
InfoSourceSocial media / Instagram / Facebook groups / twitter -0.502 0.765 0.512
InfoSourceGovernment and regulatory agencies 0.229 0.932 0.805
InfoSourceFriends / family -0.321 0.990 0.746
Dogs
InfoSourceMedia/new feeds -0.204 0.628 0.745
InfoSourceSocial media / Instagram / Facebook groups / twitter -0.135 0.700 0.847
InfoSourceGovernment and regulatory agencies 0.206 0.856 0.81
InfoSourceFriends / family 0.348 0.897 0.698
Pigs
InfoSourceMedia/new feeds -0.676 0.747 0.365
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.371 0.816 0.65
InfoSourceGovernment and regulatory agencies -0.786 1.057 0.457
InfoSourceFriends / family 0.601 1.039 0.563
Chickens
InfoSourceMedia/new feeds -0.673 0.601 0.263
InfoSourceSocial media / Instagram / Facebook groups / twitter -0.057 0.648 0.93
InfoSourceGovernment and regulatory agencies -0.624 0.856 0.466
InfoSourceFriends / family -0.448 0.901 0.619
Tigers
InfoSourceMedia/new feeds 0.171 0.764 0.823
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.078 0.838 0.926
InfoSourceGovernment and regulatory agencies -0.051 1.018 0.96
InfoSourceFriends / family 0.416 1.063 0.696

We created a new variable called InfectHumansCorrect, which means whether those people answered these questions right or wrong. Then. we pull the data of different animals together to build the following model:

  • InfectHumansCorrect ~ PetOwner + InfoSource + Animal + Wave + (1|workerId)’

  • InfectHumansCorrect (reference level is ‘wrong’)
  • Animals (refernce level is ‘Cats’)
Logistic Regression: InfectHumansCorrect ~ PetOwner + InfoSource + Animals + Wave + (1 | workerId)
Model Estimate Std. Error pvalue
PetOwnerYes 0.380 0.366 0.299
InfoSourceMedia/new feeds 0.187 0.389 0.631
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.530 0.461 0.25
InfoSourceGovernment and regulatory agencies 0.300 0.570 0.599
InfoSourceFriends / family 0.070 0.558 0.9
AnimalsDogs 0.219 0.164 0.182
AnimalsPigs 1.392 0.181 0
AnimalsChickens 1.916 0.194 0
AnimalsTigers 1.001 0.174 0
Wave4 0.117 0.123 0.342

HumansInfectAnimal 1-5

Based on your current knowledge, rate the extent to which the following animals can catch COVID-19 from humans.

Re-categorize the outcome (HumansInfectAnimal 1-5) - cannot catch from humans: Definitely/Probably/might cannot catch COVID-19 from humans - can catch from humans: Probably/Definitely can catch COVID-19 from humans

We build two models with different independent variables.

  • HumansInfectAnimal ~ PetOwner + (1|workerId)
  • HumansInfectAnimal ~ InfoSource + (1|workerId)
Coef of ‘PetOwnerYes’ in the Logistic Regression: HumansInfectAnimal ~ PetOwner + (1|workerId) for different animals
Model Estimate Std. Error pvalue
Cats
PetOwnerYes 0.596 0.365 0.102
Dogs
PetOwnerYes -0.154 0.328 0.639
Pigs
PetOwnerYes -0.732 0.355 0.039
Chickens
PetOwnerYes -1.098 0.395 0.005
Tigers
PetOwnerYes 0.311 0.403 0.44
Coef of ‘InfoSource’ in the Logistic Regression: HumansInfectAnimal ~ InfoSource + (1|workerId) for different animals
Model Estimate Std. Error pvalue
Cats
InfoSourceMedia/new feeds 0.970 0.622 0.119
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.347 0.681 0.61
InfoSourceGovernment and regulatory agencies 0.371 0.847 0.661
InfoSourceFriends / family 0.347 0.877 0.692
Dogs
InfoSourceMedia/new feeds 0.520 0.578 0.369
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.200 0.635 0.753
InfoSourceGovernment and regulatory agencies 0.234 0.793 0.768
InfoSourceFriends / family 0.862 0.821 0.294
Pigs
InfoSourceMedia/new feeds -0.575 0.622 0.356
InfoSourceSocial media / Instagram / Facebook groups / twitter -0.399 0.691 0.564
InfoSourceGovernment and regulatory agencies -0.987 0.897 0.271
InfoSourceFriends / family 0.240 0.868 0.782
Chickens
InfoSourceMedia/new feeds -0.434 0.656 0.508
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.070 0.721 0.923
InfoSourceGovernment and regulatory agencies 0.592 0.882 0.502
InfoSourceFriends / family 0.015 0.968 0.987
Tigers
InfoSourceMedia/new feeds 1.302 0.713 0.068
InfoSourceSocial media / Instagram / Facebook groups / twitter -0.564 0.808 0.486
InfoSourceGovernment and regulatory agencies 0.389 0.964 0.687
InfoSourceFriends / family 0.306 1.009 0.761

We created a new variable called HumansInfectAnimalCorrect, which means whether those people answered these questions right or wrong. Then. we pull the data of different animals together to build the following model:

  • HumansInfectAnimalCorrect ~ PetOwner + InfoSource + Animal + Wave + (1|workerId)’

  • HumansInfectAnimalCorrect (reference level is ‘wrong’)
  • Animals (refernce level is ‘Cats’)
Logistic Regression: HumansInfectAnimalCorrect ~ PetOwner + InfoSource + Animals + Wave + (1 | workerId)
Model Estimate Std. Error pvalue
PetOwnerYes 0.214 0.105 0.042
InfoSourceMedia/new feeds 0.438 0.193 0.023
InfoSourceSocial media / Instagram / Facebook groups / twitter 0.053 0.211 0.8
InfoSourceGovernment and regulatory agencies 0.157 0.263 0.55
InfoSourceFriends / family 0.171 0.280 0.541
AnimalsDogs -0.354 0.105 0.001
AnimalsPigs 2.063 0.122 0
AnimalsChickens 2.352 0.130 0
AnimalsTigers -0.402 0.105 0
Wave4 -0.077 0.076 0.312

Cat owners versus dogs owners

Common Questions

Compliance

Logistic Regression: Compliance ~ MostAttach + Wave + if_shelter + MostAttach * if_shelter + (1 | workerId)
Model Estimate Std. Error pvalue
MostAttachCat -2.063 0.709 0.004
Wave2 0.400 0.282 0.156
Wave3 0.579 0.302 0.055
Wave4 0.250 0.381 0.512
if_shelteryes -0.281 0.428 0.512
MostAttachCat:if_shelteryes -0.347 0.668 0.603

Interact

Logistic Regression: Interact ~ MostAttach + Wave + if_shelter + MostAttach * if_shelter + (1 | workerId)
Model Estimate Std. Error pvalue
MostAttachCat -1.249 0.490 0.011
Wave2 -0.035 0.218 0.872
Wave3 0.081 0.232 0.728
Wave4 -0.086 0.295 0.769
if_shelteryes 0.185 0.327 0.572
MostAttachCat:if_shelteryes 0.192 0.485 0.692

Concern

Ordinal Logistic Regression: Concern ~ MostAttach + Wave + if_shelter + MostAttach * if_shelter + (1 | workerId)
Model Estimate Std. Error pvalue
MostAttachCat 0.189 0.506 0.709
Wave2 0.017 0.186 0.928
Wave3 -0.514 0.206 0.013
Wave4 -0.227 0.261 0.384
if_shelteryes 0.417 0.325 0.2
MostAttachCat:if_shelteryes -1.039 0.435 0.017

CCAS 1-13

Ordinal Logistic Regression: CCAS ~ MostAttach + Wave + if_shelter + MostAttach*if_shelter + (1|workerId)
Model Estimate Std. Error pvalue
CCAS1: My pet/animal provides me with companionship
MostAttachCat -0.912 0.538 0.09
Wave2 0.519 0.203 0.01
Wave3 0.759 0.221 0.001
Wave4 0.898 0.286 0.002
if_shelteryes 0.107 0.343 0.756
MostAttachCat:if_shelteryes 0.529 0.460 0.25
CCAS2: Having a pet/animal gives me something to care for
MostAttachCat 0.047 0.483 0.923
Wave2 0.080 0.184 0.664
Wave3 0.149 0.200 0.457
Wave4 0.200 0.254 0.431
if_shelteryes 0.016 0.300 0.959
MostAttachCat:if_shelteryes 0.132 0.426 0.757
CCAS3: My pet/animal provides me with pleasurable activity
MostAttachCat -0.536 0.497 0.281
Wave2 0.008 0.184 0.967
Wave3 0.342 0.197 0.083
Wave4 0.391 0.256 0.126
if_shelteryes 0.044 0.308 0.886
MostAttachCat:if_shelteryes 0.328 0.423 0.438
CCAS4: My pet/animal is a source of constancy in my life
MostAttachCat 0.110 0.481 0.819
Wave2 -0.063 0.178 0.725
Wave3 0.280 0.194 0.15
Wave4 0.384 0.248 0.122
if_shelteryes -0.188 0.299 0.528
MostAttachCat:if_shelteryes 0.057 0.421 0.893
CCAS5: My pet/animal makes me feel needed
MostAttachCat -0.544 0.455 0.232
Wave2 0.235 0.168 0.163
Wave3 0.623 0.183 0.001
Wave4 0.654 0.234 0.005
if_shelteryes -0.413 0.282 0.143
MostAttachCat:if_shelteryes 0.333 0.393 0.397
CCAS6: My pet/animal makes me feel safe
MostAttachCat -1.820 0.452 0
Wave2 0.135 0.157 0.388
Wave3 0.560 0.170 0.001
Wave4 0.577 0.221 0.009
if_shelteryes 0.009 0.267 0.972
MostAttachCat:if_shelteryes -0.158 0.356 0.657
CCAS7: My pet/animal makes me play and laugh
MostAttachCat 0.044 0.470 0.925
Wave2 -0.019 0.179 0.917
Wave3 0.166 0.192 0.386
Wave4 0.322 0.247 0.193
if_shelteryes 0.365 0.295 0.216
MostAttachCat:if_shelteryes -0.307 0.412 0.457
CCAS8: Having a pet/animal gives me something to love
MostAttachCat -0.074 0.523 0.887
Wave2 0.138 0.191 0.47
Wave3 0.388 0.206 0.06
Wave4 0.429 0.262 0.101
if_shelteryes 0.156 0.317 0.623
MostAttachCat:if_shelteryes 0.348 0.440 0.429
CCAS9: I get more exercise because of my pet/animal
MostAttachCat -4.454 0.443 0
Wave2 -0.110 0.153 0.471
Wave3 0.301 0.165 0.067
Wave4 0.086 0.212 0.686
if_shelteryes -0.199 0.255 0.436
MostAttachCat:if_shelteryes 0.304 0.350 0.385
CCAS10: I get comfort from touching my pet/animal
MostAttachCat 0.221 0.531 0.677
Wave2 0.121 0.191 0.526
Wave3 0.276 0.205 0.179
Wave4 0.514 0.266 0.054
if_shelteryes 0.210 0.319 0.51
MostAttachCat:if_shelteryes 0.309 0.450 0.491
CCAS11: I enjoy watching my pet/animal
MostAttachCat 0.573 0.585 0.328
Wave2 0.606 0.210 0.004
Wave3 0.621 0.224 0.006
Wave4 1.248 0.298 0
if_shelteryes 1.132 0.348 0.001
MostAttachCat:if_shelteryes 0.109 0.490 0.824
CCAS12: My pet/animal makes me feel loved
MostAttachCat -0.376 0.517 0.467
Wave2 0.177 0.184 0.334
Wave3 0.388 0.198 0.05
Wave4 0.472 0.255 0.065
if_shelteryes 0.131 0.309 0.672
MostAttachCat:if_shelteryes -0.336 0.432 0.436
CCAS13: My pet/animal makes me feel trusted
MostAttachCat -0.311 0.422 0.461
Wave2 0.184 0.168 0.273
Wave3 0.343 0.181 0.057
Wave4 0.364 0.230 0.114
if_shelteryes -0.034 0.273 0.901
MostAttachCat:if_shelteryes 0.286 0.380 0.452

Now let’s look at the sum of CCAS 1-13

Linear mixed-effect model: CCAS ~ MostAttach + Wave + if_shelter + MostAttach * if_shelter + (1 | workerId)
Model Estimate Std. Error pvalue
MostAttachCat -3.365 1.083 0.002
Wave2 0.449 0.304 0.141
Wave3 1.372 0.328 0
Wave4 1.529 0.430 0
if_shelteryes 0.030 0.529 0.954
MostAttachCat:if_shelteryes 0.403 0.725 0.579

Unique Questions for Wave2

NonVetService and NonVetLessFreq

Model Estimate Std. Error pvalue
NonVetService ~ MostAttach
MostAttachCat -1.634 0.339 0
NonVetLessFreq ~ MostAttach
MostAttachCat 0.746 0.714 0.296

VetVisit and VetVisitFreq

Model Estimate Std. Error pvalue
VetVisit ~ MostAttach
MostAttachCat -1.239 0.250 0
VetVisitFreq ~ MostAttach
MostAttachCat -0.198 0.287 0.49

PetHealth

Estimate Std. Error Pr(>|z|)
MostAttachCat 0.163 0.444 0.713

Unique Questions for Wave3 and Wave4

AvailNonVetServ

Ordinal Logistic Regression: AvailNonVetServ ~ MostAttach + Wave + (1|workerId)
Model Estimate Std. Error pvalue
MostAttachCat -0.273 0.247 0.269
Wave4 1.008 0.182 0

UseNonVetServ

Logistic Regression: UseNonVetServ ~ MostAttach + Wave + (1|workerId)
Model Estimate Std. Error pvalue
MostAttachCat -4.053 1.705 0.017
Wave4 0.250 0.503 0.619

AvailVetServ

Ordinal Logistic Regression: AvailVetServ ~ MostAttach + Wave + (1|workerId)
Model Estimate Std. Error pvalue
MostAttachCat 0.106 0.252 0.675
Wave4 0.835 0.192 0

UsedVetServ

Logistic Regression: UsedVetServ ~ MostAttach + Wave + (1|workerId)
Model Estimate Std. Error pvalue
MostAttachCat -0.904 0.430 0.036
Wave4 0.396 0.321 0.218